title: Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance.
abstract: National legislations for the assessment of the skin sensitization potential of chemicals are increasingly based on the globally harmonized system (GHS). In this study, experimental data on 55 non-sensitizing and 45 sensitizing chemicals were evaluated according to GHS criteria and used to test the performance of computer (in silico) models for the prediction of skin sensitization. Statistic models (Vega, Case Ultra, TOPKAT), mechanistic models (Toxtree, OECD (Q)SAR toolbox, DEREK) or a hybrid model (TIMES-SS) were evaluated. Between three and nine of the substances evaluated were found in the individual training sets of various models. Mechanism based models performed better than statistical models and gave better predictivities depending on the stringency of the domain definition. Best performance was achieved by TIMES-SS, with a perfect prediction, whereby only 16% of the substances were within its reliability domain. Some models offer modules for potency; however predictions did not correlate well with the GHS sensitization subcategory derived from the experimental data. In conclusion, although mechanistic models can be used to a certain degree under well-defined conditions, at the present, the in silico models are not sufficiently accurate for broad application to predict skin sensitization potentials.
authors.affiliation: BASF Schweiz AG, Basel, Switzerland. Electronic address: wera.teubner@basf.com.
authors.forename: Wera
authors.initials: W
authors.lastname: Teubner
authors.forename: Anette
authors.initials: A
authors.lastname: Mehling
authors.forename: Paul Xaver
authors.initials: PX
authors.lastname: Schuster
authors.forename: Katharina
authors.initials: K
authors.lastname: Guth
authors.forename: Andrew
authors.initials: A
authors.lastname: Worth
authors.forename: Julien
authors.initials: J
authors.lastname: Burton
authors.forename: Bennard
authors.initials: B
authors.lastname: van Ravenzwaay
authors.forename: Robert
authors.initials: R
authors.lastname: Landsiedel
mesh_terms: D000485:Allergens / Q000737:chemistry / Q000633:toxicity*; D000826:Animal Testing Alternatives / Q000379:methods*; D000818:Animals; D003198:Computer Simulation; D017449:Dermatitis, Allergic Contact / Q000209:etiology / Q000378:metabolism; D006801:Humans; D008956:Models, Chemical; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012680:Sensitivity and Specificity; D012867:Skin / Q000187:drug effects* / Q000378:metabolism; D012882:Skin Tests / Q000379:methods
chemical_list: D000485:Allergens
keywords: (Q)SAR; ACD; AOP; Adverse outcome pathway; Case Ultra; ECHA; European Chemicals Agency; GHS; LLNA; LMW; OECD; OECD toolbox; Organisation for Economic Cooperation and Development; REACH; Registration Evaluation Authorisation and Restriction of Chemicals; Skin sensitization; TIMES-SS; TOPKAT; Toxtree; Vega; adverse outcome pathway; allergic contact dermatitis; globally harmonized system of classification and labeling of chemicals; local lymph node assay; low molecular weight; quantitative structure–activity relationship